Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 6
k
The 2 Factorial Design
Solutions
6.1. An engineer is interested in the effects of cutting speed (A), tool geometry (B), and cutting angle on
the life (in hou
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 3
Experiments with a Single Factor: The Analysis of Variance
Solutions
3.1. An experimenter has conducted a single-factor experiment with four levels of the fact
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 8
Two-Level Fractional Factorial Designs
Solutions
8.1. Suppose that in the chemical process development experiment in Problem 6.7, it was only possible
to run a
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 12
Robust Parameter Design and Process Robustness Studies
Solutions
12.1. Reconsider the leaf spring experiment in Table 12.1. Suppose that the objective is to f
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 13
Experiments with Random Factors
Solutions
13.1. An experiment was performed to investigate the capability of a measurement system. Ten parts
were randomly sel
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 9
Three-Level and Mixed-Level
Factorial and Fractional Factorial Design
Solutions
9.1. The effects of developer strength (A) and developer time (B) on the densit
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 5
Introduction to Factorial Designs
Solutions
5.1. The following output was obtained from a computer program that performed a two-factor ANOVA
on a factorial exp
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 10
Fitting Regression Models
Solutions
10.1. The tensile strength of a paper product is related to the amount of hardwood in the pulp. Ten
samples are produced i
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 7
Blocking and Confounding in the 2k Factorial Design
Solutions
7.1 Consider the experiment described in Problem 6.1. Analyze this experiment assuming that each
Solutions from Montgomery, D. C. (2008) Design and Analysis of Experiments, Wiley, NY
Chapter 4
Randomized Blocks, Latin Squares, and Related Designs
Solutions
4.1.
The ANOVA from a randomized complete block experiment output is shown below.
Source
DF
SS
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 2
Simple Comparative Experiments
Solutions
2.1. Computer output for a random sample of data is shown below. Some of the quantities are missing.
Compute the value
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY
Chapter 11
Response Surface Methods and Designs
Solutions
11.1. A chemical plant produces oxygen by liquefying air and separating it into its component gases by
fraction
Formal inference
Important feature of statistical analysis is assessment of
uncertainty in the conclusions
Math 664 Principles of Statistical Analysis
Lecture Set 10: Inference and variable selection
We are often interested in individual parameters , whic
Example G
Math 664 Methods for Statistical Consulting
Lecture 2: Case Study from Example G
Cost of construction of nuclear
power plants
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Lecture 1
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Descriptio
Preliminary analysis - what to consider
Although it is tempting to dive straight into formal statistical
analysis, it is often useful to start with a thoughtful and
systematic exploration of a new data set.
Math 664 Methods for Statistical Consulting
Lect
Broad aims in the design of studies
Avoid systematic error, i.e. errors from irrelevant sources that
do not cancel out in the long run
Math 664 Methods for Statistical Consulting
Lecture 6
Design of Studies; observational studies
Reduce non-systematic (ra
Collecting data
Math 664 Methods for Statistical Consulting
Lecture 5
Design of Studies; observational studies
Experiments and sampling
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Producing Data
Lecture 5
Confo
Example
Math 664 Principles of Statistical Analysis
Lecture 7
Example Q of Applied Statistics
Strength of cotton yarn
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Description of study
Lecture 7
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The data
Tw
Example V from Applied Statistics
Math 664 Methods Statistical Consulting
Lecture Set 9
Example V from Applied Statistics
A retrospective study with binary data
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D
Logistic regression for binary response
When the response is binary, i.e. Y can take values 0 or 1, the
mean of Y , , is equal to p P(Y = 1).
Math 664 Methods for Statistical Consulting
Lecture Set 8: Logistic and Ordinal regression
We want to model (i.e.
Newtons law
We will look at the simple example of investigating one of
Newtons laws.
Math 664 Methods for Statistical Consulting
Lecture 2: Investigating Newtons Laws
If an object is dropped from a resting position above ground,
the distance d that it wil
Introduction
Statistical analysis - understand unexplained and haphazard
variation to explain some phenomenon
Math 664 Methods for Statistical Consulting
Lecture 1: Principles for Statistical Analysis
Statistical consulting - it involves
listening
Ji Meng
Math 361, Problem set 9
Due 11/8/10
1. (2.2.3) Let X1 and X2 have the joint pdf h(x1 , x2 ) = 2ex1 x2 , 0 < x1 <
x2 < , zero elsewhere. Find the joint pdf of Y1 = 2X1 and Y2 = X2 X1 .
Answer: We have that X1 = Y1 /2 and X2 = Y2 + Y1 /2. This gives us the
Math 361, Problem Set 2
November 4, 2010
Due: 11/1/10
1. (2.1.5) Given that the nonnegqative function g (x) has the property that
g (x)dx = 1, show that
0
f (x1 , x2 ) =
2g ( x2 + x2 )
1
2
x2 + x2
1
2
0 < x1 <
,
0 < x2 < ,
zero elsewhere, satises the con
Principles of applied statistics
Principles of measurement
Math 664 Principles of Statistical Analysis
Preliminary analysis
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Principles of measurement
Lecture 5
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Methods for Statistical Consulting
Classication
Math 664 Methods for Statistical Consulting
Lecture 8
Classication and Regression Trees
Ji Meng Loh
Random Forests, Boosted Trees
New Jersey Institute of Technology
Cullimore 211C
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Lecture 8
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Principles of Statistical Analysis
Math 664 Principles of Statistical Analysis
Lecture 7
Techniques of formal inference
Ji Meng Loh
Variable selection techniques
New Jersey Institute of Technology
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Lecture 7
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Formal inference
Today
Finish the R section from last lecture
Math 664 Principles of Statistical Analysis
Example C from Applied Statistics
Ji Meng Loh
Design of studies; observational studies
New Jersey Institute of Technology
Cullimore 211C
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Lecture 4
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Pr
Methods for Statistical Consulting
Math 664 Methods for Statistical Consulting
Lecture 9
Classication and Regression Trees
Ji Meng Loh
Bagging, Random Forests, Boosted Trees
New Jersey Institute of Technology
Cullimore 211C
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Lecture 9
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Tree
Introduction
Statistical analysis - understand unexplained and haphazard
variation to explain some phenomenon
Math 664 Methods for Statistical Consulting
Statistical consulting - it involves
Ji Meng Loh
listening
asking questions
understanding
computer sk